Research on neural network model based on maximum conditional entropy and its application on short term load forecasting

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

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A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

Mathematical Problems in Engineering ◽

10.1155/2020/1428104 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Lizhen Wu ◽

Chun Kong ◽

Xiaohong Hao ◽

Wei Chen

Keyword(s):

Neural Network ◽

Power System ◽

Network Model ◽

Neural Network Model ◽

Feature Vector ◽

Load Forecasting ◽

Percentage Error ◽

Short Term ◽

Hybrid Neural Network ◽

Short Term Load Forecasting

Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.

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An Improved RBF Neural Network for Short Term Load Forecasting

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1008-1009.709 ◽

2014 ◽

Vol 1008-1009 ◽

pp. 709-713 ◽

Cited By ~ 1

Author(s):

Chuang Li ◽

Zhi Qiang Liang ◽

Min You Chen

Keyword(s):

Neural Network ◽

Network Model ◽

Discriminant Function ◽

Neural Network Model ◽

Rbf Neural Network ◽

Load Forecasting ◽

Static Model ◽

Short Term ◽

Dynamic Neural Network ◽

Short Term Load Forecasting

Neural network is widely used in the load forecasting area,but the traditional methods of load forecasting usually base on static model,which cannot change as time goes on. And the accuracy is worse and worse. To solve the problem, a dynamic neural network model for load forecasting is proposed .By way of introduce Error discriminant function, to control the error of load forecasting and dynamically modify the predicting model. Through the contrast of the short-term load forecasting result based on static neural network model and dynamic neural network model proposed, the error of load forecasting is decrease effectively.

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